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Multimodal foundation models for material property prediction and discovery

Newton 2025年第1期1卷

作者： Viggo Moro Charlotte Loh Rumen Dangovski Ali Ghorashi Andrew Ma Zhuo Chen Samuel Kim Peter Y. Lu Thomas Christensen Marin Soljačić Department of Physics Massachusetts Institute of Technology Cambridge MA 02139 USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA 02139 USA Research and Exploratory Development John Hopkins University Applied Physics Laboratory Laurel MD 20723 USA Data Science Institute University of Chicago Chicago IL 60637 USA Department of Electrical and Photonics Engineering Technical University of Denmark 2800 Kongens Lyngby Denmark

Artificial intelligence is transforming computational materials science by improving property prediction and accelerating the discovery of novel materials. Recently, publicly available material data repositories have grown rapidly, encompassing not only more materials but also a greater variety and quantity of their associated properties. Existing machine-learning efforts in materials science focus primarily on single-modality tasks, i.e., relationships between materials and a single physical property, thus not taking advantage of the rich multimodal data available. Here, we introduce multimodal learning for materials (MultiMat), a framework enabling self-supervised multimodal training of foundation models for materials. Using the Materials Project database, we demonstrate the potential of MultiMat by: (1) achieving state-of-the-art performance for challenging material property prediction tasks; (2) enabling novel and accurate material discovery via latent-space similarity, allowing screening for stable materials with desired properties; and (3) encoding emergent features that correlate with material properties and may provide novel scientific insights.

关键词： multimodal learning foundation models machine learning for materials science material property prediction material discovery contrastive learning representation learning self-supervised learning

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Roadmap on data-Centric Materials science

arXiv

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arXiv 2024年

作者： Bauer, Stefan Benner, Peter Bereau, Tristan Blum, Volker Boley, Mario Carbogno, Christian Catlow, C.A. Richard Dehm, Gerhard Eibl, Sebastian Ernstorfer, Ralph Fekete, Ádám Foppa, Lucas Fratzl, Peter Freysoldt, Christoph Gault, Baptiste Ghiringhelli, Luca M. Giri, Sajal K. Gladyshev, Anton Goyal, Pawan Hattrick-Simpers, Jason Kabalan, Lara Karpov, Petr Khorrami, Mohammad S. Koch, Christoph Kokott, Sebastian Kosch, Thomas Kowalec, Igor Kremer, Kurt Leitherer, Andreas Li, Yue Liebscher, Christian H. Logsdail, Andrew J. Lu, Zhongwei Luong, Felix Marek, Andreas Merz, Florian Mianroodi, Jaber R. Neugebauer, Jörg Pei, Zongrui Purcell, Thomas A.R. Raabe, Dierk Rampp, Markus Rossi, Mariana Rost, Jan-Michael Saal, James Saalmann, Ulf Sasidhar, Kasturi Narasimha Saxena, Alaukik Sbailò, Luigi Scheidgen, Markus Schloz, Marcel Schmidt, Daniel F. Teshuva, Simon Trunschke, Annette Wei, Ye Weikum, Gerhard Xian, R. Patrick Yao, Yi Yin, Junqi Zhao, Meng Scheffler, Matthias School of Computation Information and Technology Technical University of Munich & Helmholtz AI Munich Germany Max Planck Institute for Dynamics of Complex Technical Systems Magdeburg Germany Institute for Theoretical Physics Heidelberg University Heidelberg Germany Thomas Lord Department of Mechanical Engineering and Materials Science Duke University DurhamNC United States Monash University Department of Data Science and AI Melbourne Australia The NOMAD Laboratory The FHI of the Max-Planck-Gesellschaft IRIS-Adlershof of the Humboldt-Universität zu Berlin Berlin Germany School of Chemistry Cardiff University Cardiff United Kingdom Max-Planck-Institut für Eisenforschung GmbH Düsseldorf Germany Max Planck Computing and Data Facility Garching Germany Institute for Optics and Atomic Physics Technical University of Berlin Berlin Germany Physics Department and IRIS-Adlershof Humboldt Universität zu Berlin Berlin Germany Max Planck Institute of Colloids and Interfaces Potsdam Germany Department of Materials Imperial College London United Kingdom Department of Materials Science and Engineering Friedrich-Alexander Universität Erlangen-Nürnberg Germany Department of Chemistry Northwestern University Illinois United States Humboldt-Universität zu Berlin Department of Physics Berlin Germany Department of Materials Science and Engineering University of Toronto Toronto Canada STFC Hartree Centre Daresbury Laboratory Daresbury Warrington United Kingdom Molecular Simulations from First Principles e.V. Berlin Germany Humboldt-Universität zu Berlin Department of Computer Science Berlin Germany Max Planck Institute for Polymer Research Mainz Germany ICFO-Institut de Ciencies Fotoniques The Barcelona Institute of Science and Technology Castelldefels Barcelona Spain Lenovo HPC Innovation Center Stuttgart Germany New York University New York United States University of Arizona Biochemistry Department Arizona United States Max Planck Institute for the Structure an

science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm’ of materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of Artificial Intelligence (AI) and its subset Machine Learning (ML), has become pivotal in addressing all these challenges. This Roadmap on data-Centric Materials science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research, and experimental techniques like photoemission, atom probe tomography, and electron microscopy. While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research. © 2024, CC BY.

关键词： data Analytics

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Enhancing skin cancer detection through category representation and fusion of pre-trained models

引用

Information Fusion 2025年

作者： Lingping Kong Juan D. Velasquez Václav Snášel Millie Pant Jeng-Shyang Pan Jana Nowakova Department of Computer Science VSB-Technical University of Ostrava listpodu 17 Czech Republic Department of Industrial Engineering University of Chile Santiago O’Higgins 1058 Chile Instituto Sistemas Complejos de Ingeniería Santiago Chile Department of Applied Mathematics and Scientific Computing Indian Institute of Technology Roorkee Uttarakhand 247667 India Mehta Family School of Data Science and Artificial Intelligence Indian Institute of Technology Roorkee Uttarakhand 247667 India School of Artificial Intelligence Nanjing University of Information Science and Technology 219 Ningliu Road China College of Computer Science and Engineering Shandong University of Science and Technology China

The use of pre-trained models in medical image classification has gained significant attention due to their ability to handle complex datasets and improve accuracy. However, challenges such as domain-specific customization, interpretability, and computational efficiency remain critical, especially in high-stakes applications such as skin cancer detection. In this paper, we introduce a novel interpretability-assisted fine-tuning framework that leverages category representation to enhance both model accuracy and transparency. Using the widely known HAM10000 dataset, which includes seven imbalanced categories of skin cancer, we demonstrate that our method improves the classification accuracy by 2.6% compared to standard pre-trained models. In addition to precision, we also achieve significant improvements in interpretability, with our category representation framework providing more understandable insights into the model’s decision-making process. Key metrics, such as precision and recall, show enhanced performance, particularly for underrepresented skin cancer types such as Melanocytic Nevi (F1 score of 0.94) and Actinic Keratosis (F1 score of 0.66). Furthermore, the prediction accuracy of the proposed model of the top-3 reaches 98. 21%, which is highly significant for medical decision making. This observation in interpredibility underscores the value of top- n predictions, especially in challenging cases, to support more informed and accurate decisions. The proposed fusion framework not only enhances predictive accuracy, but also offers an interpretable model output that can assist clinicians in making informed decisions. This makes our approach particularly relevant in medical applications, where both accuracy and transparency are crucial. Our results highlight the potential of fusing pretrained models with category representation techniques to bridge the gap between performance and interpretability in AI-driven healthcare solutions.

关键词： Pre-trained model Sparse representation Skin cancer data Model interpredibility

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From Whole-slide Image to Biomarker Prediction: A Protocol for End-to-End Deep Learning in Computational Pathology

arXiv

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arXiv 2023年

作者： El Nahhas, Omar S.M. van Treeck, Marko Wölflein, Georg Unger, Michaela Ligero, Marta Lenz, Tim Wagner, Sophia J. Hewitt, Katherine J. Khader, Firas Foersch, Sebastian Truhn, Daniel Kather, Jakob Nikolas Else Kroener Fresenius Center for Digital Health Medical Faculty Carl Gustav Carus Technical University Dresden Dresden Germany School of Computer Science University of St Andrews St Andrews United Kingdom Helmholtz Munich German Research Center for Environment and Health Munich Germany School of Computation Information and Technology Technical University of Munich Munich Germany Department of Diagnostic and Interventional Radiology University Hospital Aachen Aachen Germany Institute of Pathology University Medical Center Mainz Mainz Germany Pathology & Data Analytics Leeds Institute of Medical Research at St James's University of Leeds Leeds United Kingdom University Hospital Heidelberg Heidelberg Germany Department of Medicine 1 University Hospital Faculty of Medicine Carl Gustav Carus Technische Universität Dresden Dresden Germany

Hematoxylin- and eosin (H&E) stained whole-slide images (WSIs) are the foundation of diagnosis of cancer. In recent years, development of deep learning-based methods in computational pathology enabled the prediction of biomarkers directly from WSIs. However, accurately linking tissue phenotype to biomarkers at scale remains a crucial challenge for democratizing complex biomarkers in precision oncology. This protocol describes a practical workflow for solid tumor associative modeling in pathology (STAMP), enabling prediction of biomarkers directly from WSIs using deep learning. The STAMP workflow is biomarker agnostic and allows for genetic- and clinicopathologic tabular data to be included as an additional input, together with histopathology images. The protocol consists of five main stages which have been successfully applied to various research problems: formal problem definition, data preprocessing, modeling, evaluation and clinical translation. The STAMP workflow differentiates itself through its focus on serving as a collaborative framework that can be used by clinicians and engineers alike for setting up research projects in the field of computational pathology. As an example task, we applied STAMP to the prediction of microsatellite instability (MSI) status in colorectal cancer, showing accurate performance for the identification of MSI-high tumors. Moreover, we provide an open-source codebase which has been deployed at several hospitals across the globe to set up computational pathology workflows. The STAMP workflow requires one workday of hands-on computational execution and basic command line knowledge. © 2023, CC BY.

关键词： Pathology

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Geometric analysis of fast-slow PDEs with fold singularities via Galerkin discretisation

arXiv

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arXiv 2022年

作者： Engel, Maximilian Hummel, Felix Kuehn, Christian Popović, Nikola Ptashnyk, Mariya Zacharis, Thomas Department of Mathematics and Computer Science Freie Universität Berlin Arnimallee 14 Berlin14195 Germany Department of Mathematics Technical University of Munich Boltzmannstraße 3 Garching bei München85748 Germany Munich Data Science Institute Walther-von-Dyck Straße 10 Garching bei München85748 Germany Complexity Science Hub Vienna Josefstädter Straße 39 Vienna1080 Austria School of Mathematics Maxwell Institute for Mathematical Sciences University of Edinburgh James Clerk Maxwell Building King’s Buildings Peter Guthrie Tait Road EdinburghEH9 3FD United Kingdom School of Mathematical & Computer Sciences Maxwell Institute for Mathematical Sciences Heriot-Watt University EdinburghEH14 4AP United Kingdom Korteweg–de Vries Institute for Mathematics University of Amsterdam Science Park 105-107 Amsterdam1098 XG Netherlands

We study a singularly perturbed fast-slow system of two partial differential equations of reaction-diffusion type on a bounded domain via Galerkin discretisation. We assume that the reaction kinetics in the fast variable realise a generic fold singularity, whereas the slow variable takes the role of a dynamic bifurcation parameter, thus extending the classical analysis of the singularly perturbed fold. Our approach combines a spectral Galerkin discretisation with techniques from geometric singular perturbation theory which are applied to the resulting high-dimensional systems of ordinary differential equations. In particular, we show the existence of invariant slow manifolds in the phase space of the original system of PDEs away from the fold singularity, while the passage past the singularity of the Galerkin manifolds obtained after discretisation is described by geometric desingularisation, or blow-up. Finally, we discuss the relation between these Galerkin manifolds and the underlying slow *** Codes 35B25, 35K57, 34Cxx, 34D15, 34E15, 37G10 © 2022, CC BY.

关键词： Perturbation techniques

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Transformers in Medical Image Analysis: A Review

arXiv

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arXiv 2022年

作者： He, Kelei Gan, Chen Li, Zhuoyuan Rekik, Islem Yin, Zihao Ji, Wen Gao, Yang Wang, Qian Zhang, Junfeng Shen, Dinggang Medical School of Nanjing University Nanjing China National Institute of Healthcare Data Science Nanjing University Nanjing China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China BASIRA Lab Faculty of Computer and Informatics Engineering Istanbul Technical University Istanbul Turkey School of Science and Engineering Computing University of Dundee United Kingdom School of Biomedical Engineering ShanghaiTech University Shanghai China Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai China

Transformers have dominated the field of natural language processing, and recently impacted the computer vision area. In the field of medical image analysis, Transformers have also been successfully applied to full-stack clinical applications, including image synthesis/reconstruction, registration, segmentation, detection, and diagnosis. Our paper aims to promote awareness and application of Transformers in the field of medical image analysis. Specifically, we first overview the core concepts of the attention mechanism built into Transformers and other basic components. Second, we review various Transformer architectures tailored for medical image applications and discuss their limitations. Within this review, we investigate key challenges revolving around the use of Transformers in different learning paradigms, improving the model efficiency, and their coupling with other techniques. We hope this review can give a comprehensive picture of Transformers to the readers in the field of medical image analysis. © 2022, CC BY-SA.

关键词： Diagnosis

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Improved small gain conditions for input-to-state stability with respect to measurement functions: Discrete time networked system

Improved small gain conditions for input-to-state stability ...

引用

IEEE Conference on Decision and Control

作者： Yuanqiu Mo Changbin Yu Soura Dasgupta Westlake Institute for Advanced Study Westlake University Hangzhou China College of Artificial Intelligence and Big Data Shandong First Medical University & Shandong Academy of Medical Sciences Jinan China Institute for Intelligent Robots Fudan University Shanghai China Shandong Computer Science Center Shandong Provincial Key Laboratory of Computer Networks China Faculty of Engineering & the Built Environment University of Johannes-Burg Johannesburg South Africa University of Iowa Iowa City Iowa USA

ISBN: (纸本)9781665436601

In this paper we study input-to-state stability with respect to measurement functions for discrete time networked systems. In such a networked system, the trajectory of each subsystem is affected by another in each time step, and this constraining system may change from step to step. We derive small gain conditions of input-to-state stability with respect to measurement functions for this type of discrete time networked system relying on the construction of dissipative-form finite time Lyapunov function. These conditions loosen those presented in [1] and [2]. Finally, we demonstrate the applicability of our results by applying them to the stability analysis of a distributed graph algorithm which does not satisfy the conditions of [1] and [2].

关键词： Network topology Conferences Gain measurement Stability analysis Time measurement Trajectory Topology

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EXPLAINABLE ARTIFICIAL INTELLIGENCE (XAI) 2.0: A MANIFESTO OF OPEN CHALLENGES AND INTERDISCIPLINARY RESEARCH DIRECTIONS

arXiv

引用

arXiv 2023年

作者： Longo, Luca Brcic, Mario Cabitza, Federico Choi, Jaesik Confalonieri, Roberto Ser, Javier Del Guidotti, Riccardo Hayashi, Yoichi Herrera, Francisco Holzinger, Andreas Jiang, Richard Khosravi, Hassan Lecue, Freddy Malgieri, Gianclaudio Páez, Andrés Samek, Wojciech Schneider, Johannes Speith, Timo Stumpf, Simone The Artificial Intelligence and Cognitive Load Research Lab Technological University Dublin Ireland University of Zagreb Faculty of Electrical Engineering and Computing Croatia University of Milano-Bicocca Milan Italy IRCCS Ospedale Galeazzi Sant’Ambrogio Milan Italy Kim Jaechul Graduate School of AI Korea Advanced Institute of Science & Technology Korea Republic of INEEJI Corporation Korea Republic of Department of Mathematics University of Padua Italy Derio Spain Bilbao Spain University of Pisa Pisa Italy Department of Computer Science Meiji University Tokyo Japan Department of Computer Science and Artificial Intelligence DaSCI Andalusian Institute in Data Science & Computational Intelligence University of Granada Granada Spain Human-Centered AI Lab University of Natural Resources and Life Sciences Vienna Austria School of Computing and Communications Lancaster University United Kingdom The University of Queensland Brisbane Australia Sophia Antipolis France eLaw Center for Law and Digital Technologies Leiden University Netherlands Department of Philosophy Universidad de los Andes Bogotá Colombia Center for Research & Formation in Artificial Intelligence Universidad de los Andes Bogotá Colombia Technical University of Berlin Berlin Germany Fraunhofer Heinrich Hertz Institute Berlin Germany Berlin Germany Department of Information Systems and Computer Science University of Liechtenstein Liechtenstein Liechtenstein Department of Philosophy University of Bayreuth Bayreuth Germany Center for Perspicuous Computing Saarland University Saarbrücken Germany School of Computing Science University of Glasgow United Kingdom

As systems based on opaque Artificial Intelligence (AI) continue to flourish in diverse real-world applications, understanding these black box models has become paramount. In response, Explainable AI (XAI) has emerged as a field of research with practical and ethical benefits across various domains. This paper not only highlights the advancements in XAI and its application in real-world scenarios but also addresses the ongoing challenges within XAI, emphasizing the need for broader perspectives and collaborative efforts. We bring together experts from diverse fields to identify open problems, striving to synchronize research agendas and accelerate XAI in practical applications. By fostering collaborative discussion and interdisciplinary cooperation, we aim to propel XAI forward, contributing to its continued success. Our goal is to put forward a comprehensive proposal for advancing XAI. To achieve this goal, we present a manifesto of 27 open problems categorized into nine categories. These challenges encapsulate the complexities and nuances of XAI and offer a road map for future research. For each problem, we provide promising research directions in the hope of harnessing the collective intelligence of interested stakeholders. © 2023, CC BY-SA.

关键词： Ethical technology

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UAV Communication Channel Measurement, Modeling, and Application

引用

Journal of Communications and Information networks 2019年第4期4卷 32-43页

作者： Xiang Cheng Yiran Li Lin Bai State Key Laboratory of Advanced Optical Communication Systems and Networks Department of ElectronicsSchool of Electronics Engineering and Computer SciencePeking UniversityBeijing 100871China Shenzhen Research Institute of Big Data Shenzhen 518000China School of Cyber Science and Technology Beihang UniversityBeijing 100191China

As unmanned aerial vehicles(UAV)deploy-ment is shifting from military to civilian sectors,and in particular as a means of facilitating more flexible and versatile wireless communications,the UAV channels that were previously not well understood are attracting increasing research and *** the key component facilitating the UAV-aided communications,UAV channel characteristics and modeling are of critical importance to the designed UAV system to achieve satisfactory *** this article,we will provide a comprehensive overview and the future perspective of envisioned UAV communication system benefits,the existing and needed UAV channel measurements and modeling approaches,together with the new viewpoint of UAV channel applications in different communication and networking *** existing work and future directions are extensively covered.

关键词： UAV channel measurements modeling application future challenges

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Emerging Technologies for Next Generation Remote Health Care and Assisted Living

TechRxiv

引用

TechRxiv 2022年

作者： Ahmad, Ijaz Asghar, M. Zeeshan Kumar, Tanesh Li, Gaolei Manzoor, Ahsan Mikhaylov, Konstantin Shah, Syed Attique Höyhtyä, Marko Reponen, Jarmo Huusko, Jyrki Harjula, Erkki The VTT Technical Research Centre of Finland Espoo02150 Finland Navigil Espoo Finland The University of Oulu Oulu Finland The Institute of Cyber Security Shanghai Jiao Tong University Shanghai200240 China Rovio Entertainment Ltd. Espoo Finland The Data Systems Group Institute of Computer Science University of Tartu Delta Centre Narva mnt 18-3123 Tartu51009 Estonia The Research Unit of Medical Imaging Physics and Technology University of Oulu Finland The Medical Research Centre Oulu Oulu University Hospital University of Oulu Finland

Remote health care is currently one of the most promising solutions to ensure a high level of treatment outcome, cost-efficiency and sustainability of the healthcare systems worldwide. Even though research on remote health care can be traced back to the early days of the Internet, the recent COVID-19 has necessitated further improvement in existing health care systems with invigorated research on remote health care technologies. In this article we delve into the state-of-the-art research in latest technologies and technological paradigms that play a vital role in enabling the next generation remote health care and assisted living. First the need of using the latest technological developments in the domain of remote health care is briefly discussed. Then the most important technologies and technological paradigms that are crucial in enabling remote health care and assisted living are emphasised. Henceforth, a detailed survey of existing technologies, potential challenges in those technologies, and possible solutions is conducted. Finally, missing research gaps and important future research directions in each enabling technology are brought forth to motivate further research in remote health care. © 2022, CC BY.

关键词： Internet of things

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